Four ways Intelligent Automation can accelerate value in the Insurance enterprise

With digital innovation, the insurance industry can gain significant benefits, both on the balance sheet and with customer equity, but face substantial difficulty in implementing them. In addition, the abrasive effects of the pandemic, slow recovery of the global economy, and pressure from insurtech entrants are forcing insurance enterprise executives to look for new ways to unlock value. 

Enter intelligent automation. If you look deep enough, the insurance industry is heavily process-oriented, where tasks are repetitive. These tasks add substantial cost, time, and effort on the wrong side of the balance sheet. Robotic process automation or RPA is already making substantial inroads within the industry where tasks that follow a set process without significant deviations are beeing automated. These tasks help free up human time for more value-adding functions such as account management and improving customer satisfaction. 

Intelligent automation aims to augment RPA with more learning and decision-making abilities. The touted benefits speak for themselves – reduced response time, higher operational efficiency, improved worker performance, and reduced operational risk. 

Here we look at four areas core areas of Insurance that can drive IPA adaptability. 

Faster claims processing with machine learning driven intelligent automation

The last couple of years have seen machine learning and language processing algorithms make quantum leaps. Better processing power, reduced cost, and easy accessibility have given rise to better learning algorithms and advanced the industry faster. Supervised and unsupervised learning are critical concepts within machine learning. In supervised learning, algorithms recognize outputs based on a set of existing inputs and recognize outputs when new inputs are available. In unsupervised learning, algorithms learn to recognize patterns by observing structured data. 

However, the challenge lies in understanding unstructured data. For example, adjusters for claims approval processes often leave notes that are not in any standard format. They may also include photos and notes from other entities. This unstructured data is indigestible by computers. It also mandates that a human look through them and decide their validity and eventual impact on the claims process. This process is a highly labor-intensive task. However, with advances in natural language processing technology (NLP), these algorithms can go through documents and understand their content and context much as a human does. 

Post this, concepts such as transfer learning come into play. The same models used to train the algorithm to recognize unstructured data transfer the learnings to any downstream task that can now be completed in less time with the same efficiency and accuracy. 

Improved underwriting with Natural language processing and sentiment analysis 

One of the most labor-intensive processes in Insurance is underwriting. Underwriters must pour over essential data such as social media posts, photos, business documents, regulatory filings, and many more to make sense of the risk factors before insuring an individual, business, or asset. 

Increasingly improving NLP technology can “read” documents and text across many data sources and give inference scores. At the same time, sentiment analysis technologies can derive an underlying positive or negative sentiment from these data sources. 

Machine learning algorithms learn from these inferences and score them, thus reducing the time to conclude. One of the most prominent issues in the underwriting process is getting an accurate picture of the applicant’s loss or claim history. This history is usually gleaned from loss run reports. RPA algorithms in tandem with NLP can significantly reduce the time to collate reports and data that underwriting engines can process. This benefit makes the underwriting process an excellent candidate for intelligent process automation. 

Value-adding frontend functions with chatbots 

One of the most significant gripes we as customers have is our insurance providers’ long response times. According to a survey by Globant, 29% of customer-service queries surround a claims request, and 36% of queries are for more routine activities such as policy renewals and reminders. This insight is an opportunity for insurance incumbents to leverage technology to improve customer satisfaction and free up agents to drive more meaningful tasks such as upselling and reducing churn. 

Chatbots are a variant of intelligent automation and can accomplish most of these tasks. Chatbots have been around for some time now. However, the early generation of chatbots was clunky, inaccurate, and ended up frustrating customers. However, with rapid improvements in AI and NLP technology, chatbots are fast becoming the preferred first point of contact in customer service.

For insurance providers, RPA can accomplish the most routine and straightforward tasks. These tasks would also involve structuring data better such that it is accurate and more useful for upstream applications. For example, with today’s NLP algorithms, chatbots can better understand what customers ask, and give accurate answers. The goal here is to reduce the time needed for a successful interaction. 

Rapidly adapt to compliance regulation changes using AI-driven intelligent automation

In many countries, especially the United States, Insurance is a highly regulated industry. As a result, insurance companies need to comply with a significant number of regulations. When these regulations change, insurance providers must adapt and reorganize their business processes to comply. Unfortunately, these changes are often manual and are error-prone. This error can unwittingly lead to data breaches and often defeat the very goals of compliance. 

AI-based algorithms can maintain and research customer data better and faster. Moreover, they maintain a complete log of their activities, leaving a complete audit trail. This trail lets insurance providers monitor their compliance in real-time. They can even have better internal audits than the external audits they are often required to comply with.